Data storage has increased by leaps and bounds within organizations. As a result, storage is growing exponentially and databases/data storage mechanisms have proliferated both in numbers and variety. Organizations/enterprises may use different types of databases, for example, Online Transaction Processing (OLTP) databases, analytic structures, such as Data Warehouses, dependent data marts, Operational Data Stores (ODS), independent data marts, de-normalized databases for reporting, replicated databases, and Online Analytical Processing (OLAP) servers across various subject areas and business processes. These databases may grow into various types and size over a period of time, leading to complexity in managing databases as well as information stored on these databases, thereby requiring rationalization.
In conventional methods, assessments for rationalization are performed manually or through Excel based sheets. However, these methods do not apply an objective approach towards data and database rationalization. As a result, the assessment is not optimal from the perspective of an enterprise or organization. Moreover, as the assessments are done manually, they are time consuming and thus fail to provide proper assessment in a desired time frame.